Richard Smith (2011) and the Graybill Bristlecones

Richard Smith’s new paper doesn’t mention Graybill bristlecones, but once again, his paper does nothing more than discover what we already knew – that Graybill bristlecones have a HS shape. In the process, Smith amusingly discovers a “divergence” problem with lake sediments

Smith’s new paper describes the use of the methodology of his earlier paper to the “new” dataset used in McShane and Wyner 2010. Smith says that his earlier paper “used the NOAMER tree ring dataset, which consists of 70 temperature series constructed from tree rings for 581 years (1400-1980).” In the preceding post, I observed that the data set in question consisted of tree ring chronologies, which cannot be assumed to be “temperature series”.

The McShane-Wyner dataset considered by Smith consists of 93 Mann et al 2008 series that go back to 1000. Smith attributes the seeming instability of reconstructions to lake sediment records (observing that there are 12 within the dataset), pondering the possibility of “divergence” problems in lake sediments – a possibility that, according to Smith’s belief, had evaded the keen eye of paleos.

Smith defines divergence as follows:

Paleoclimatologists have coined the term “divergence” to describe cases in which the stationarity assumption appears to be breaking down within the timescale of observational data.

While this is a sensible definition, I’m not sure that this accurately characterizes its application by the Team, where the phenomenon is in practice limited to series that don’t go up. Smith’s definition would include series that go up too much. Rather than these examples being perceived as examples of “divergence”; they are welcomed by the Team.

Smith continues:

The best known example of divergence concerns trees; see for example Briffa et al. 1998 or pages 48-52 of North et al. (2006). However, the problem does not (so far as is known) apply uniformly to all tree-ring proxies; the specific class of proxies for which it is known to be a problem are tree-ring latewood density records. However, most of the known records of this type go back no further than AD 1400; in particular, none of them are among the 93 proxies used in the present analysis (Dr. Michael Mann, personal communication). Therefore, it appears that the known divergence problem with tree rings is not responsible for the results in the present paper.

Smith attributes the instability to lake sediments, of which there are 12 in the McShane-Wyner dataset, two of which, as CA readers are well aware, are upside-down Tiljander series, the modern portion of which is hugely contaminated by bridgebuilding and agriculture. Smith suggests that non-stationarity in lake sediment series might be a problem – noting that, to his knowledge, this possibility had not been previously considered.

To the best of my knowledge, no previous study has explicitly identified lake sediment records as subject to this problem, though with the benefit of hindsight, it seems obvious that lake sediment deposits in the late 20th century would be affected by anthropogenic activity other than increasing CO2.

Thousands of blog readers around the world are familiar with the fact that Mann used the modern (contaminated) portion of the Tiljander series, ironically upside down. Ross and I even went to the trouble of reporting this in a short comment in PNAS (not cited by Smith on this point.)

In this case, Smith is not complaining about the Tiljander sediments going up too much. Actually his complaint is the opposite. Some of the sediment series in the Mann 2008 data set have a pronounced medieval warm period.

Smith therefore examines a reduced dataset of 81 proxies using inverse regression on principal components and once again gets a characteristic HS shape – one that looks for all the world like the original Mann reconstruction.

There’s a simple reason. Smith once again has created a bristlecone reconstruction. The 81 series in the new data set include 18 Graybill bristlecone chronologies, ALL of which were in the 70-series NOAMER dataset of his previous paper.

Last time, Smith had 20 Graybill bristlecones out of 70. This time, the Graybill bristlecones constitute 18 of 81 series in the data set. Surprise, surprise, he gets the same answer.

12 Comments

A minor thing:
The two paragraphs near the end without a capital letter at the beginning, which start with “given that the” and “t has also been suggested” seem to be out of place. They reproduce Smith’s words, and methinks they should be deleted, or highlighted as citations and commented upon. Part of these texts is discussed earlier in this post.

Smith concludes that the rate of temperature increase currently observed has also been present at regular intervals in the past. Hence Mann’s proxy data indicate that the current rate of temperature change is not unusual.

This would seem to be a different conclusion to that reached by Mann. Perhaps their first-year graduate courses in statistics differed?

His comments about the lake sediments are foreshadowed in my recent post Kill It With Fire on Mann 2008, the first versions of which were published in 2008 here and here on CA. In “Kill It With Fire” you can see that the sediments divide into a couple of groups, with the lake sediments having a very different appearance than the other sediments.

But as Steve points out, their New! Improved! mathematics misses the point. All that any of these mathematical methods can do is to assign weights to the individual proxies. I hold that in the marked absence of any theoretical reason to weight any proxy over another, a simple average is the appropriate test.

But even that doesn’t solve the problem. When you have a number of proxies that cancel each other out, it only takes a few proxies with a common shape to affect even a simple average. So all of the discussion of the math is misdirection. The die is cast as soon as one selects the proxies, which is why (as Steve has pointed out more than once) ex ante rules of proxy selection are absolutely necessary.

Thanks for your interesting analysis of this, Steve, and for keeping the “choice of math vs. choice of proxies” question in play.

On page 15, Smith (2011) says, “several of the proxies which contribute heavily to PC2 [Fig. 6(b)] are of the “lake sediment” type (data codes 4000, 4001)… Of the 93 proxies being used in the current analysis, 12 are of lake sediment type.”

On page 4, Smith (2011) states that he used Mann’s proxy data, and gives Prof. Mann’s Penn State URL. He also says he archived the relevant datasets at his own webpage. At the latter site, the file proxynames.txt lists the 93 proxies used in the analysis. The file proxytype.txt gives the codes assigned as per the S.I. Mann et al (2008). Putting the two together, looking at codes 4000 and 4001, and referring back to the Mann et al S.I. —

Of the Tiljander proxiesdata series, Smith thus used darksum and xraydenseave. He indicates that he used the same 93 proxies as McShayne and Wyner (2010), who in turn followed Mann et al (2008).

Of the fourthree Tiljander data series, Mann et al (2008) screened all four — thicknessmm, lightsum, darksum, and xraydenseave. The first three passed their validation criteria and were used in the reconstructions; xraydenseave did not and thus wasn’t used.

I am unclear on this apparent discrepancy between Smith (2011) and Mann et al (2008).

Details on the Tiljander series are available at this post. As a refresher, she measured these properties of each varve:
* its thickness (millimeters)
* the effective thickness of the inorganic (mineral) component (millimeters)
* its absorbance of X-Rays (arbitrary units)

Tiljander et al then deduced the contribution of organic matter to each varve by this formula:

In their 2003 paper, Tiljander et al cautioned that the varves were progressively contaminated from about 1720 through the end of the 20th century by local activities such as farming, lake eutrophication, and road construction.

Tiljander et al (2003)’s interpretations of XRD, lightsum, and darksum are plausible, but not necessarily correct. My own view is that these series aren’t suited for use as temperature proxies, as discussed at CA last year and reproduced here. Search for “Regarding another question” and note that the Little Ice Age is clearly visible in the profile of Chironomid fossils from nearby Lake Hamptrask, but not in any of the Tiljander data series.

McShane Wyner used all the series as at AD100 not just the ones that “passed” screening.

This had the curious result of including way more Graybill bristlecone chronologies than in the Mann 2008 screened data set. As I recall, only one or two bristlecone series passed the Mann screening test. Simple principal components applied to the screened AD1000 M08 network might yield quite different results.

> McShane Wyner used all the series as at AD100 not just the ones that “passed” screening.

Hmmm. Then it seems to me that “all four” Tiljander data series should have qualified for M&W10. They all go back well past 1 AD.
Steve: there’s something about this in the article. I think that the collinearity was a problem and so they combined some of the Tiljs.

From the 19th century on, various human activities (Anthropogenic Local Development, in other words) led to progressively greater amounts of mineral silt and organic matter settling to the bottom of Lake Korttajarvi. So thickness went way up, lightsum went way up, and darksum went way up. XRD bounced around and went up.

That’d be co-linearity.

One thing that wasn’t co-linear was local temperature, as recorded by the nearest weather station. According to Tiljander03’s Figure 2, that was flattish, 1881-1993.

Happily, the computed average temperature for the 5 deg x 5 deg CRUTEM3v gridcell that covers the area did rise (link).